We develop an all-optical platform integrating a universal optothermal rotation technique with a standard optical microscope to drive the out-of-plane rotation of an arbitrary organism for its high-resolution volumetric visualization with reduced optical shadowing, occlusion and scattering effect. Furthermore, when coupled with machine learning for the classification of cells of high similarity, our volumetric imaging technique can collect large numbers of unique images for each cell and therefore reduce sample quantities required for the machine learning training. Impressively, we can improve the cell classification accuracy while using one-tenth the number of samples.
Machine learning (ML) has emerged in recent years as a data-driven approach for photonic inverse design. Despite their impressive performance in finding abstract mappings between the design parameters and optical properties, ML algorithms suffer from a high likelihood of slow converging when there exist multiple designs giving similar optical responses. Here we adopt a deep convolutional mixture density neural network, which models the design as a mixture of Gaussian distributions rather than discrete values, to address the non-uniqueness issue. An example of layered structures consisting of alternating oxides under arbitrary incidence conditions is present to showcase the proof of concept.
We have developed a versatile optothermal microrobot platform that enables low-power optical manipulations of variable synthetic particles and biological cells. An Internet-based interface has been developed to allow user(s) to manipulate the microrobots from their smartphones, laptops and desktops from anywhere at any time, enabling connected workspaces for anywhere productivity. Five manipulation modes (i.e., rotating, rolling, pushing, pulling and braking) have been achieved, which can be switched on-demand for the variable tasks. The multimodal and nanoscale manipulation of the robots enables in situ single-cell characterizations to achieve three-dimensional cellular imaging and membrane protein profiling.
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